Mistakes to avoid in your DS/AI/ML Projects #datascience #shorts #ai #coding #webdevelopment #ml

Here’s how you can enhance your projects and make them stand out:

📊 SQL: Instead of using CSV files for data storage, integrate SQL databases to manage and handle your data more efficiently.

🚀 MLOps: Implement MLOps techniques to streamline your machine learning project workflow, from development to production.

🌐 Flask: Switch from Streamlit to Flask to build more flexible and scalable web applications.

📈 Experiment Tracking: Use tools like MLFlow, Kubeflow, or DVC to track the entire lifecycle of your machine learning models, ensuring transparency and version control.

🐳 Docker: Containerize your applications with Docker, making them easy to deploy and run in different environments.

🔧 CI/CD Deployment: Automate the deployment process using GitHub Actions or Jenkins to ensure continuous integration and delivery.

☁️ Cloud Deployment: Deploy your models on the cloud using platforms like AWS, Azure, or GCP to make them accessible and scalable.
.
.

#trending #trendingreels #coding #codinglife #tech #placement #succes #interview #jobinterview #programming #datascience #dataanalytics #datascientist #jobs #machinelearning #interviewtips #ınstamood #ai

How to become a Data Scientist | How to learn coding | How to become a ML Engineer | How to be a Data Analyst | How to become a Software Engineer | How to crack Data Science interviews | How to crack tech interviews